Creating ensembles of generative adversarial network discriminators for one-class classification

Ermaliuc, M. and Stamate, D. and Magoulas, George and Pu, I. (2021) Creating ensembles of generative adversarial network discriminators for one-class classification. In: 22nd International Conference on Engineering Applications of Neural Networks, 25-27 June 2021, Crete, Greece (online).

Abstract

We introduce an algorithm for one-class classification based on binary classification of the target class against synthetic samples. We use a process inspired by Generative Adversarial Networks (GANs) in order to both acquire synthetic samples and to build the one-class classifier. The first objective is achieved by leading the generator’s output into close vicinities of the target class region. For the second objective, we obtain a one-class classifier by generating an ensemble of discriminators obtained from the GAN’s training process. Our approach is tested on publicly available datasets producing promising results when compared to other methods.

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